Overview

Dataset statistics

Number of variables55
Number of observations2920
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory589.0 B

Variable types

Categorical34
DateTime3
Numeric18

Alerts

is_FC has constant value "0"Constant
is_SG has constant value "0"Constant
is_GS has constant value "0"Constant
is_PL has constant value "0"Constant
is_IC has constant value "0"Constant
is_UP has constant value "0"Constant
is_VA has constant value "0"Constant
is_DU has constant value "0"Constant
is_DS has constant value "0"Constant
is_PO has constant value "0"Constant
is_SA has constant value "0"Constant
is_SS has constant value "0"Constant
is_PY has constant value "0"Constant
is_DR has constant value "0"Constant
is_SH has constant value "0"Constant
is_FZ has constant value "0"Constant
is_PR has constant value "0"Constant
is_BL has constant value "0"Constant
intensity_light has constant value "0"Constant
AvgSpeed is highly overall correlated with ResultSpeedHigh correlation
Cool is highly overall correlated with DewPoint and 5 other fieldsHigh correlation
DewPoint is highly overall correlated with Cool and 5 other fieldsHigh correlation
Heat is highly overall correlated with Cool and 5 other fieldsHigh correlation
ResultSpeed is highly overall correlated with AvgSpeedHigh correlation
SeaLevel is highly overall correlated with StnPressureHigh correlation
StnPressure is highly overall correlated with SeaLevelHigh correlation
Tavg is highly overall correlated with Cool and 5 other fieldsHigh correlation
Tmax is highly overall correlated with Cool and 5 other fieldsHigh correlation
Tmin is highly overall correlated with Cool and 5 other fieldsHigh correlation
WetBulb is highly overall correlated with Cool and 5 other fieldsHigh correlation
day_length_min is highly overall correlated with dayofyear and 2 other fieldsHigh correlation
dayofyear is highly overall correlated with day_length_min and 2 other fieldsHigh correlation
intensity_heavy is highly overall correlated with is_FGHigh correlation
is_FG is highly overall correlated with intensity_heavyHigh correlation
month is highly overall correlated with day_length_min and 2 other fieldsHigh correlation
week is highly overall correlated with day_length_min and 2 other fieldsHigh correlation
is_GR is highly imbalanced (99.6%)Imbalance
is_DZ is highly imbalanced (73.4%)Imbalance
is_SN is highly imbalanced (97.9%)Imbalance
is_FG is highly imbalanced (89.6%)Imbalance
is_HZ is highly imbalanced (56.4%)Imbalance
is_FU is highly imbalanced (97.3%)Imbalance
is_SQ is highly imbalanced (98.8%)Imbalance
is_MI is highly imbalanced (99.2%)Imbalance
is_BC is highly imbalanced (98.5%)Imbalance
is_VC is highly imbalanced (93.4%)Imbalance
intensity_heavy is highly imbalanced (92.4%)Imbalance
Heat has 1859 (63.7%) zerosZeros
Cool has 1145 (39.2%) zerosZeros
PrecipTotal has 1566 (53.6%) zerosZeros

Reproduction

Analysis started2026-01-07 23:47:13.586111
Analysis finished2026-01-07 23:48:00.492575
Duration46.91 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Station
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
1
1464 
2
1456 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Length

2026-01-07T23:48:00.590605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:00.665815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Most occurring characters

ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11464
50.1%
21456
49.9%

Date
Date

Distinct1471
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Minimum2007-05-01 00:00:00
Maximum2014-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-07T23:48:00.757643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:48:00.899227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tmax
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.517808
Minimum5
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:01.033484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12.2
Q120.6
median25.6
Q329.4
95-th percentile33.3
Maximum40
Range35
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation6.3759046
Coefficient of variation (CV)0.26005198
Kurtosis-0.27895448
Mean24.517808
Median Absolute Deviation (MAD)4.4
Skewness-0.55578693
Sum71592
Variance40.65216
MonotonicityNot monotonic
2026-01-07T23:48:01.166561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.9126
 
4.3%
26.1121
 
4.1%
27.8118
 
4.0%
27.2114
 
3.9%
28.3109
 
3.7%
29.4106
 
3.6%
26.7105
 
3.6%
2599
 
3.4%
30.697
 
3.3%
3097
 
3.3%
Other values (53)1828
62.6%
ValueCountFrequency (%)
51
 
< 0.1%
5.61
 
< 0.1%
6.75
 
0.2%
7.25
 
0.2%
7.89
0.3%
8.39
0.3%
8.911
0.4%
9.412
0.4%
1019
0.7%
10.618
0.6%
ValueCountFrequency (%)
401
 
< 0.1%
39.42
 
0.1%
38.92
 
0.1%
38.34
 
0.1%
37.82
 
0.1%
37.25
 
0.2%
36.75
 
0.2%
36.16
 
0.2%
35.611
0.4%
3516
0.5%

Tmin
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.339658
Minimum-1.7
Maximum28.3
Zeros7
Zeros (%)0.2%
Negative13
Negative (%)0.4%
Memory size22.9 KiB
2026-01-07T23:48:01.288440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7
5-th percentile4.4
Q110
median15
Q318.9
95-th percentile22.8
Maximum28.3
Range30
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation5.7728828
Coefficient of variation (CV)0.40258164
Kurtosis-0.57195767
Mean14.339658
Median Absolute Deviation (MAD)4.4
Skewness-0.35456402
Sum41871.8
Variance33.326176
MonotonicityNot monotonic
2026-01-07T23:48:01.420836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.2121
 
4.1%
18.3111
 
3.8%
15.6108
 
3.7%
16.1105
 
3.6%
16.7105
 
3.6%
18.9103
 
3.5%
13.9102
 
3.5%
20101
 
3.5%
1599
 
3.4%
17.899
 
3.4%
Other values (44)1866
63.9%
ValueCountFrequency (%)
-1.76
 
0.2%
-0.67
 
0.2%
07
 
0.2%
0.610
 
0.3%
1.112
0.4%
1.711
 
0.4%
2.220
0.7%
2.821
0.7%
3.322
0.8%
3.929
1.0%
ValueCountFrequency (%)
28.31
 
< 0.1%
27.82
 
0.1%
27.23
 
0.1%
26.73
 
0.1%
26.19
 
0.3%
25.64
 
0.1%
2520
0.7%
24.46
 
0.2%
23.928
1.0%
23.329
1.0%

Tavg
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.428562
Minimum1.9
Maximum34.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:01.572996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile8.6
Q115.6
median20.3
Q323.9
95-th percentile27.5
Maximum34.2
Range32.3
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation5.8628115
Coefficient of variation (CV)0.30176251
Kurtosis-0.42927209
Mean19.428562
Median Absolute Deviation (MAD)4.1
Skewness-0.4672118
Sum56731.4
Variance34.372558
MonotonicityNot monotonic
2026-01-07T23:48:01.715799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.571
 
2.4%
20.866
 
2.3%
22.866
 
2.3%
2562
 
2.1%
21.960
 
2.1%
20.660
 
2.1%
23.658
 
2.0%
21.757
 
2.0%
25.657
 
2.0%
2055
 
1.9%
Other values (104)2308
79.0%
ValueCountFrequency (%)
1.91
 
< 0.1%
2.21
 
< 0.1%
2.51
 
< 0.1%
2.81
 
< 0.1%
3.11
 
< 0.1%
3.31
 
< 0.1%
3.61
 
< 0.1%
3.93
0.1%
4.21
 
< 0.1%
4.44
0.1%
ValueCountFrequency (%)
34.21
 
< 0.1%
33.61
 
< 0.1%
33.11
 
< 0.1%
32.81
 
< 0.1%
32.53
0.1%
31.92
0.1%
31.73
0.1%
31.41
 
< 0.1%
31.11
 
< 0.1%
30.83
0.1%

DewPoint
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.915548
Minimum-5.6
Maximum23.9
Zeros22
Zeros (%)0.8%
Negative93
Negative (%)3.2%
Memory size22.9 KiB
2026-01-07T23:48:01.852908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.6
5-th percentile1.1
Q17.8
median12.2
Q316.7
95-th percentile20.6
Maximum23.9
Range29.5
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation5.9370477
Coefficient of variation (CV)0.49826057
Kurtosis-0.49385077
Mean11.915548
Median Absolute Deviation (MAD)4.4
Skewness-0.42941097
Sum34793.4
Variance35.248535
MonotonicityNot monotonic
2026-01-07T23:48:01.988894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15127
 
4.3%
12.2124
 
4.2%
12.8113
 
3.9%
15.6112
 
3.8%
16.1109
 
3.7%
11.1100
 
3.4%
13.398
 
3.4%
18.397
 
3.3%
11.796
 
3.3%
16.791
 
3.1%
Other values (44)1853
63.5%
ValueCountFrequency (%)
-5.61
 
< 0.1%
-53
 
0.1%
-4.43
 
0.1%
-3.94
 
0.1%
-3.37
 
0.2%
-2.88
 
0.3%
-2.25
 
0.2%
-1.717
0.6%
-1.122
0.8%
-0.623
0.8%
ValueCountFrequency (%)
23.91
 
< 0.1%
23.32
 
0.1%
22.89
 
0.3%
22.215
 
0.5%
21.737
1.3%
21.143
1.5%
20.669
2.4%
2063
2.2%
19.488
3.0%
18.974
2.5%

WetBulb
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.174075
Minimum0
Maximum25.6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:02.125195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q111.7
median16.1
Q319.4
95-th percentile22.2
Maximum25.6
Range25.6
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation5.1608651
Coefficient of variation (CV)0.34011068
Kurtosis-0.48599932
Mean15.174075
Median Absolute Deviation (MAD)3.3
Skewness-0.47488799
Sum44308.3
Variance26.634528
MonotonicityNot monotonic
2026-01-07T23:48:02.263829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
17.2134
 
4.6%
18.3131
 
4.5%
15128
 
4.4%
17.8121
 
4.1%
16.1121
 
4.1%
16.7116
 
4.0%
19.4116
 
4.0%
18.9113
 
3.9%
15.6111
 
3.8%
20.6107
 
3.7%
Other values (37)1722
59.0%
ValueCountFrequency (%)
01
 
< 0.1%
0.65
 
0.2%
1.15
 
0.2%
1.78
 
0.3%
2.29
 
0.3%
2.85
 
0.2%
3.317
0.6%
3.915
0.5%
4.431
1.1%
519
0.7%
ValueCountFrequency (%)
25.61
 
< 0.1%
258
 
0.3%
24.48
 
0.3%
23.920
 
0.7%
23.349
1.7%
22.855
1.9%
22.271
2.4%
21.797
3.3%
21.1102
3.5%
20.6107
3.7%

Heat
Real number (ℝ)

High correlation  Zeros 

Distinct30
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8994178
Minimum0
Maximum16.1
Zeros1859
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:02.378905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.8
95-th percentile9.4
Maximum16.1
Range16.1
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation3.3111586
Coefficient of variation (CV)1.7432492
Kurtosis2.3519397
Mean1.8994178
Median Absolute Deviation (MAD)0
Skewness1.8004652
Sum5546.3
Variance10.963772
MonotonicityNot monotonic
2026-01-07T23:48:02.520164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
01859
63.7%
2.288
 
3.0%
0.686
 
2.9%
1.181
 
2.8%
4.467
 
2.3%
1.765
 
2.2%
2.861
 
2.1%
8.357
 
2.0%
3.949
 
1.7%
6.749
 
1.7%
Other values (20)458
 
15.7%
ValueCountFrequency (%)
01859
63.7%
0.686
 
2.9%
1.181
 
2.8%
1.765
 
2.2%
2.288
 
3.0%
2.861
 
2.1%
3.345
 
1.5%
3.949
 
1.7%
4.467
 
2.3%
546
 
1.6%
ValueCountFrequency (%)
16.12
 
0.1%
15.62
 
0.1%
152
 
0.1%
14.44
 
0.1%
13.95
 
0.2%
13.37
 
0.2%
12.815
0.5%
12.212
0.4%
11.719
0.7%
11.128
1.0%

Cool
Real number (ℝ)

High correlation  Zeros 

Distinct30
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.134726
Minimum0
Maximum16.1
Zeros1145
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:02.637416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.2
Q35.6
95-th percentile9.4
Maximum16.1
Range16.1
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation3.3925285
Coefficient of variation (CV)1.0822408
Kurtosis-0.38716861
Mean3.134726
Median Absolute Deviation (MAD)2.2
Skewness0.78573081
Sum9153.4
Variance11.509249
MonotonicityNot monotonic
2026-01-07T23:48:02.758315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
01145
39.2%
4.4137
 
4.7%
2.8116
 
4.0%
6.7115
 
3.9%
5.6109
 
3.7%
3.3107
 
3.7%
5107
 
3.7%
3.9104
 
3.6%
2.2103
 
3.5%
7.2101
 
3.5%
Other values (20)776
26.6%
ValueCountFrequency (%)
01145
39.2%
0.692
 
3.2%
1.189
 
3.0%
1.798
 
3.4%
2.2103
 
3.5%
2.8116
 
4.0%
3.3107
 
3.7%
3.9104
 
3.6%
4.4137
 
4.7%
5107
 
3.7%
ValueCountFrequency (%)
16.11
 
< 0.1%
15.61
 
< 0.1%
151
 
< 0.1%
14.44
 
0.1%
13.92
 
0.1%
13.34
 
0.1%
12.84
 
0.1%
12.29
0.3%
11.716
0.5%
11.116
0.5%

Sunrise
Date

Distinct129
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Minimum2026-01-07 04:15:00
Maximum2026-01-07 06:23:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-07T23:48:02.892555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:48:03.048569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sunset
Date

Distinct167
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Minimum2026-01-07 16:45:00
Maximum2026-01-07 19:31:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-07T23:48:03.192738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:48:03.339258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PrecipTotal
Real number (ℝ)

Zeros 

Distinct167
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3506815
Minimum0
Maximum174.24
Zeros1566
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:03.473325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.52
95-th percentile20.0825
Maximum174.24
Range174.24
Interquartile range (IQR)1.52

Descriptive statistics

Standard deviation10.020632
Coefficient of variation (CV)2.9906249
Kurtosis78.257707
Mean3.3506815
Median Absolute Deviation (MAD)0
Skewness6.9996889
Sum9783.99
Variance100.41306
MonotonicityNot monotonic
2026-01-07T23:48:03.624681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01566
53.6%
0.03315
 
10.8%
0.25126
 
4.3%
0.5163
 
2.2%
0.7646
 
1.6%
1.0235
 
1.2%
1.2732
 
1.1%
2.0328
 
1.0%
3.0527
 
0.9%
1.5226
 
0.9%
Other values (157)656
22.5%
ValueCountFrequency (%)
01566
53.6%
0.03315
 
10.8%
0.25126
 
4.3%
0.5163
 
2.2%
0.7646
 
1.6%
1.0235
 
1.2%
1.2732
 
1.1%
1.5226
 
0.9%
1.7823
 
0.8%
2.0328
 
1.0%
ValueCountFrequency (%)
174.241
< 0.1%
168.661
< 0.1%
120.141
< 0.1%
100.841
< 0.1%
92.961
< 0.1%
92.461
< 0.1%
84.071
< 0.1%
80.521
< 0.1%
80.011
< 0.1%
77.981
< 0.1%

StnPressure
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.68243
Minimum966.8
Maximum1011.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:03.765942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum966.8
5-th percentile982.685
Q1988.5
median991.5
Q3995.3
95-th percentile1000.3
Maximum1011.2
Range44.4
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation5.3759513
Coefficient of variation (CV)0.0054210412
Kurtosis0.72081663
Mean991.68243
Median Absolute Deviation (MAD)3.4
Skewness-0.27895223
Sum2895712.7
Variance28.900853
MonotonicityNot monotonic
2026-01-07T23:48:03.903820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993.6127
 
4.3%
991.5124
 
4.2%
990.9122
 
4.2%
989.2107
 
3.7%
992.6106
 
3.6%
989.8103
 
3.5%
994.294
 
3.2%
995.990
 
3.1%
995.387
 
3.0%
988.186
 
2.9%
Other values (93)1874
64.2%
ValueCountFrequency (%)
966.81
< 0.1%
968.21
< 0.1%
969.51
< 0.1%
970.91
< 0.1%
971.61
< 0.1%
972.22
0.1%
972.91
< 0.1%
973.21
< 0.1%
973.61
< 0.1%
974.31
< 0.1%
ValueCountFrequency (%)
1011.21
 
< 0.1%
1008.81
 
< 0.1%
1006.11
 
< 0.1%
1005.85
0.2%
1005.13
 
0.1%
1004.74
0.1%
1004.44
0.1%
1004.19
0.3%
1003.75
0.2%
1003.45
0.2%

SeaLevel
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.968164
Minimum29.23
Maximum30.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:04.040145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29.23
5-th percentile29.7
Q129.87
median29.97
Q330.06
95-th percentile30.24
Maximum30.53
Range1.3
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15876813
Coefficient of variation (CV)0.0052978929
Kurtosis0.66747489
Mean29.968164
Median Absolute Deviation (MAD)0.1
Skewness-0.17832315
Sum87507.04
Variance0.025207318
MonotonicityNot monotonic
2026-01-07T23:48:04.179257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3095
 
3.3%
29.9485
 
2.9%
29.9885
 
2.9%
29.9282
 
2.8%
29.8982
 
2.8%
30.0581
 
2.8%
29.9180
 
2.7%
29.9580
 
2.7%
30.0280
 
2.7%
29.9379
 
2.7%
Other values (91)2091
71.6%
ValueCountFrequency (%)
29.231
 
< 0.1%
29.251
 
< 0.1%
29.342
0.1%
29.431
 
< 0.1%
29.441
 
< 0.1%
29.454
0.1%
29.461
 
< 0.1%
29.471
 
< 0.1%
29.482
0.1%
29.52
0.1%
ValueCountFrequency (%)
30.531
 
< 0.1%
30.521
 
< 0.1%
30.411
 
< 0.1%
30.42
 
0.1%
30.391
 
< 0.1%
30.382
 
0.1%
30.374
0.1%
30.364
0.1%
30.357
0.2%
30.343
0.1%

ResultSpeed
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.196096
Minimum0.2
Maximum38.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:04.312454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile3.1
Q16.9
median10.3
Q314.8
95-th percentile21.7
Maximum38.8
Range38.6
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.7715334
Coefficient of variation (CV)0.51549518
Kurtosis0.74915314
Mean11.196096
Median Absolute Deviation (MAD)3.9
Skewness0.73417019
Sum32692.6
Variance33.310598
MonotonicityNot monotonic
2026-01-07T23:48:04.450233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.549
 
1.7%
10.346
 
1.6%
1042
 
1.4%
8.541
 
1.4%
7.938
 
1.3%
7.737
 
1.3%
8.436
 
1.2%
10.136
 
1.2%
6.336
 
1.2%
13.436
 
1.2%
Other values (179)2523
86.4%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.31
 
< 0.1%
0.53
 
0.1%
0.63
 
0.1%
0.83
 
0.1%
17
0.2%
1.111
0.4%
1.35
0.2%
1.44
 
0.1%
1.66
0.2%
ValueCountFrequency (%)
38.81
< 0.1%
36.51
< 0.1%
36.41
< 0.1%
35.11
< 0.1%
34.91
< 0.1%
34.41
< 0.1%
341
< 0.1%
33.51
< 0.1%
32.72
0.1%
31.91
< 0.1%

ResultDir
Real number (ℝ)

Distinct36
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.517123
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:04.588979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median19
Q325
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.053491
Coefficient of variation (CV)0.57392362
Kurtosis-1.1583278
Mean17.517123
Median Absolute Deviation (MAD)8
Skewness-0.06644472
Sum51150
Variance101.07268
MonotonicityNot monotonic
2026-01-07T23:48:04.714468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
21156
 
5.3%
3138
 
4.7%
19137
 
4.7%
23137
 
4.7%
24121
 
4.1%
4120
 
4.1%
20118
 
4.0%
22116
 
4.0%
5111
 
3.8%
6109
 
3.7%
Other values (26)1657
56.7%
ValueCountFrequency (%)
162
2.1%
2107
3.7%
3138
4.7%
4120
4.1%
5111
3.8%
6109
3.7%
797
3.3%
868
2.3%
962
2.1%
1044
 
1.5%
ValueCountFrequency (%)
3671
2.4%
3537
1.3%
3449
1.7%
3333
1.1%
3246
1.6%
3161
2.1%
3064
2.2%
2971
2.4%
2878
2.7%
2782
2.8%

AvgSpeed
Real number (ℝ)

High correlation 

Distinct177
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.805822
Minimum2.7
Maximum42.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:05.517796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile7.1
Q110.1
median13
Q316.7
95-th percentile23.2
Maximum42.3
Range39.6
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.0603633
Coefficient of variation (CV)0.36653836
Kurtosis1.3398781
Mean13.805822
Median Absolute Deviation (MAD)3.3
Skewness0.90159237
Sum40313
Variance25.607277
MonotonicityNot monotonic
2026-01-07T23:48:05.662898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.163
 
2.2%
9.359
 
2.0%
11.954
 
1.8%
1349
 
1.7%
11.347
 
1.6%
14.844
 
1.5%
12.943
 
1.5%
12.442
 
1.4%
9.742
 
1.4%
10.941
 
1.4%
Other values (167)2436
83.4%
ValueCountFrequency (%)
2.71
 
< 0.1%
3.11
 
< 0.1%
3.21
 
< 0.1%
3.42
 
0.1%
3.71
 
< 0.1%
3.91
 
< 0.1%
4.21
 
< 0.1%
4.35
0.2%
4.51
 
< 0.1%
4.72
 
0.1%
ValueCountFrequency (%)
42.31
< 0.1%
37.31
< 0.1%
37.21
< 0.1%
36.91
< 0.1%
36.41
< 0.1%
35.61
< 0.1%
34.61
< 0.1%
34.42
0.1%
33.32
0.1%
32.51
< 0.1%

year
Real number (ℝ)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.4935
Minimum2007
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:05.759445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12008
median2010
Q32012
95-th percentile2014
Maximum2014
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2909235
Coefficient of variation (CV)0.0011394832
Kurtosis-1.2358343
Mean2010.4935
Median Absolute Deviation (MAD)2
Skewness0.0047131683
Sum5870641
Variance5.2483304
MonotonicityIncreasing
2026-01-07T23:48:05.850543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2010368
12.6%
2007366
12.5%
2008366
12.5%
2012366
12.5%
2009365
12.5%
2014365
12.5%
2011364
12.5%
2013360
12.3%
ValueCountFrequency (%)
2007366
12.5%
2008366
12.5%
2009365
12.5%
2010368
12.6%
2011364
12.5%
2012366
12.5%
2013360
12.3%
2014365
12.5%
ValueCountFrequency (%)
2014365
12.5%
2013360
12.3%
2012366
12.5%
2011364
12.5%
2010368
12.6%
2009365
12.5%
2008366
12.5%
2007366
12.5%

month
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4982877
Minimum5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:05.937603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median7
Q39
95-th percentile10
Maximum10
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7116564
Coefficient of variation (CV)0.22827297
Kurtosis-1.26633
Mean7.4982877
Median Absolute Deviation (MAD)1.5
Skewness0.0031228697
Sum21895
Variance2.9297675
MonotonicityNot monotonic
2026-01-07T23:48:06.022053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10494
16.9%
5493
16.9%
7493
16.9%
8490
16.8%
6477
16.3%
9473
16.2%
ValueCountFrequency (%)
5493
16.9%
6477
16.3%
7493
16.9%
8490
16.8%
9473
16.2%
10494
16.9%
ValueCountFrequency (%)
10494
16.9%
9473
16.2%
8490
16.8%
7493
16.9%
6477
16.3%
5493
16.9%

week
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.833562
Minimum17
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:06.119756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile19
Q124
median31
Q337
95-th percentile43
Maximum44
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.6065709
Coefficient of variation (CV)0.24669777
Kurtosis-1.195405
Mean30.833562
Median Absolute Deviation (MAD)7
Skewness0.004418337
Sum90034
Variance57.859922
MonotonicityNot monotonic
2026-01-07T23:48:06.230039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
19112
 
3.8%
20112
 
3.8%
21112
 
3.8%
22112
 
3.8%
27112
 
3.8%
23112
 
3.8%
43112
 
3.8%
31112
 
3.8%
41112
 
3.8%
42112
 
3.8%
Other values (18)1800
61.6%
ValueCountFrequency (%)
176
 
0.2%
1881
2.8%
19112
3.8%
20112
3.8%
21112
3.8%
22112
3.8%
23112
3.8%
24111
3.8%
25111
3.8%
26111
3.8%
ValueCountFrequency (%)
4454
1.8%
43112
3.8%
42112
3.8%
41112
3.8%
40110
3.8%
39110
3.8%
38111
3.8%
37110
3.8%
36110
3.8%
35111
3.8%

dayofyear
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.71781
Minimum121
Maximum305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:06.352591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile130
Q1167
median213
Q3259
95-th percentile296
Maximum305
Range184
Interquartile range (IQR)92

Descriptive statistics

Standard deviation53.166094
Coefficient of variation (CV)0.24993721
Kurtosis-1.2025712
Mean212.71781
Median Absolute Deviation (MAD)46
Skewness0.0044650889
Sum621136
Variance2826.6336
MonotonicityNot monotonic
2026-01-07T23:48:06.495841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12316
 
0.5%
12816
 
0.5%
12516
 
0.5%
12716
 
0.5%
12616
 
0.5%
12916
 
0.5%
13216
 
0.5%
13116
 
0.5%
13016
 
0.5%
13816
 
0.5%
Other values (175)2760
94.5%
ValueCountFrequency (%)
12112
0.4%
12214
0.5%
12316
0.5%
12415
0.5%
12516
0.5%
12616
0.5%
12716
0.5%
12816
0.5%
12916
0.5%
13016
0.5%
ValueCountFrequency (%)
3054
 
0.1%
30416
0.5%
30316
0.5%
30216
0.5%
30116
0.5%
30016
0.5%
29916
0.5%
29816
0.5%
29716
0.5%
29616
0.5%

day_length_min
Real number (ℝ)

High correlation 

Distinct279
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean819.32705
Minimum623
Maximum914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 KiB
2026-01-07T23:48:06.638309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum623
5-th percentile646.95
Q1748
median854
Q3897
95-th percentile912
Maximum914
Range291
Interquartile range (IQR)149

Descriptive statistics

Standard deviation89.557009
Coefficient of variation (CV)0.10930557
Kurtosis-0.82174991
Mean819.32705
Median Absolute Deviation (MAD)53
Skewness-0.74264637
Sum2392435
Variance8020.4579
MonotonicityNot monotonic
2026-01-07T23:48:06.786450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912110
 
3.8%
91169
 
2.4%
91362
 
2.1%
91456
 
1.9%
91047
 
1.6%
90746
 
1.6%
90143
 
1.5%
90842
 
1.4%
90440
 
1.4%
89938
 
1.3%
Other values (269)2367
81.1%
ValueCountFrequency (%)
6232
 
0.1%
62412
0.4%
6253
 
0.1%
6265
 
0.2%
62710
0.3%
6284
 
0.1%
6292
 
0.1%
63012
0.4%
6312
 
0.1%
63214
0.5%
ValueCountFrequency (%)
91456
1.9%
91362
2.1%
912110
3.8%
91169
2.4%
91047
1.6%
90934
 
1.2%
90842
 
1.4%
90746
1.6%
90633
 
1.1%
90524
 
0.8%

is_FC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:06.923827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:06.995804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_TS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2569 
1
351 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

Length

2026-01-07T23:48:07.078661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:07.153315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

Most occurring characters

ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02569
88.0%
1351
 
12.0%

is_GR
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2919 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

Length

2026-01-07T23:48:07.246850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:07.317203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02919
> 99.9%
11
 
< 0.1%

is_RA
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
1895 
1
1025 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01895
64.9%
11025
35.1%

Length

2026-01-07T23:48:07.401242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:07.473546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01895
64.9%
11025
35.1%

Most occurring characters

ValueCountFrequency (%)
01895
64.9%
11025
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01895
64.9%
11025
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01895
64.9%
11025
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01895
64.9%
11025
35.1%

is_DZ
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2788 
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

Length

2026-01-07T23:48:07.562251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:07.633408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

Most occurring characters

ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02788
95.5%
1132
 
4.5%

is_SN
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2914 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

Length

2026-01-07T23:48:07.733194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:07.821702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02914
99.8%
16
 
0.2%

is_SG
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:07.958438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:08.053305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_GS
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:08.180964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:08.276474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_PL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:08.388835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:08.482719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_IC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:08.601118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:08.696672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_FG
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2880 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

Length

2026-01-07T23:48:08.816246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:08.917868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

Most occurring characters

ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02880
98.6%
140
 
1.4%

is_BR
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2115 
1
805 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

Length

2026-01-07T23:48:09.033159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:09.135221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

Most occurring characters

ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02115
72.4%
1805
 
27.6%

is_UP
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:09.257752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:09.345846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_HZ
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2658 
1
 
262

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

Length

2026-01-07T23:48:09.461789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:09.580437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

Most occurring characters

ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02658
91.0%
1262
 
9.0%

is_FU
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2912 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

Length

2026-01-07T23:48:09.731638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:09.848180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

Most occurring characters

ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02912
99.7%
18
 
0.3%

is_VA
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:09.981172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.088432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_DU
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:10.216439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.316643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_DS
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:10.393847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.458687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_PO
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:10.540390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.609036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_SA
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:10.694088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.763573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_SS
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:10.845563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:10.926040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_PY
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:11.007817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.072128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_SQ
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2917 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

Length

2026-01-07T23:48:11.150796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.219460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02917
99.9%
13
 
0.1%

is_DR
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:11.303608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.370955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_SH
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:11.468155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.536865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_FZ
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:11.617428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.685219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_MI
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2918 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

Length

2026-01-07T23:48:11.764551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:11.837675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02918
99.9%
12
 
0.1%

is_PR
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:11.932974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.004961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_BC
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2916 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

Length

2026-01-07T23:48:12.088336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.158323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02916
99.9%
14
 
0.1%

is_BL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:12.247325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.314196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

is_VC
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2897 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

Length

2026-01-07T23:48:12.396211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.471686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

Most occurring characters

ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02897
99.2%
123
 
0.8%

intensity_heavy
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2893 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

Length

2026-01-07T23:48:12.573791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.650930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

Most occurring characters

ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02893
99.1%
127
 
0.9%

intensity_light
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.7 KiB
0
2920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2920
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02920
100.0%

Length

2026-01-07T23:48:12.743247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-07T23:48:12.814311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02920
100.0%

Most occurring characters

ValueCountFrequency (%)
02920
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02920
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02920
100.0%

Interactions

2026-01-07T23:47:57.789381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:17.194684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.356160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.330498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.240691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.590420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.513994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.290605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.784351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.805409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.001612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.033505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.056208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.010545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.116124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.163758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.142823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:54.877494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:57.902723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:17.929229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.451435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.428448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.351330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.701609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.621544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.457714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.896467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.917950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.113164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.144135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.160272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.118378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.244616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.264887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.251445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:54.988752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.005286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.079582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.541443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.524906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.446405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.803234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.724294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.612432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.003762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.010758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.242836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.246704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.270524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.213455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.363234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.370336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.359791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.093615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.108612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.222020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.633353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.620888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.551030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.920929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.839750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.788315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.111601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.106798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.354569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.355109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.371800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.315566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.472926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.470618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.467752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.220883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.213751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.368248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.740501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.725024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.647050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.028681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.960320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.965332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.226157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.569593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.482291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.465051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.482430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.426143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.584965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.580220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.569164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.406672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.314778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.515068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.832915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.832230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.743129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.133956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.085220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.126029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.347085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.662822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.594143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.584394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.607359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.541881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.687954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.680547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.667614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.573225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.436141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.665678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.934378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.943775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.873620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.239293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.196486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.314448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.466838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.768295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.710578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.698644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:42.843344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.663951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.813385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.788424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.771915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.740104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.540398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.816212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.031587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.062055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.357148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.351518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.305784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.484560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.579667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.871237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.833208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.817479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:43.509039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.789678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.939717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.897290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.875302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:55.900719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.643395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:18.968680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.132173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.174312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.464620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.455515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.415559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.625736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.694224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:36.971408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:38.955922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:40.930365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:43.684258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:46.921107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.060898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.023019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.980496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.081525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.743664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.110240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.227507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.277070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.567878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.557355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.523354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.733663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.802210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.063333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.060847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.034623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:43.843685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.030942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.162203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.126210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.077329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.249228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.849888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.280870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.534826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.387494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.682666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.660693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:29.634331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.844155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:34.915467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.161259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.172056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.143805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:44.359061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.145566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.267931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.236618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.195304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.409765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:58.968997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.442881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.634457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.501574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.797724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.765221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.173926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:32.957524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.034045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.270026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.289518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.263070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:44.675781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.268872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.382724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.354412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.309436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.568279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.077104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.617849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.751497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.605240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:25.923926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:27.874310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.346349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.072015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.148324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.379926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.395365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.390619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.361479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.393734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.494328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.469095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.414526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.733495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.177932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.791883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.850336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.722776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.036833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.000662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.500315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.187219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.257470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.494486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.514136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.501042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.467618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.512309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.602932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.583216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.522205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:56.901122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.282745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:19.952222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:21.948582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.848688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.151495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.113599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.658768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.333185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.384241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.600715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.624047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.628334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.578243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.638896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.717886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.699392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.635349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:57.086042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.389082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.047933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.039843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:23.945163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.253740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.219500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.800638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.440503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.483658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.698039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.726301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.733938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.681931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.757819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.821533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.806313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:53.741613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:57.246204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.492428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.145694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.133961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.038150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.364124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.313062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:30.949553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.551125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.588178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.793179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.823599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.838670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.784719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:47.873693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:49.929594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:51.908497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:54.662923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:57.442800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:59.603414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:20.249899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:22.236382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:24.141386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:26.478292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:28.415873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:31.128466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:33.667904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:35.695958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:37.898177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:39.929700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:41.949112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:45.910034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:48.000463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:50.056402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:52.026789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:54.768270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-07T23:47:57.620769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-07T23:48:12.929210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AvgSpeedCoolDewPointHeatPrecipTotalResultDirResultSpeedSeaLevelStationStnPressureTavgTmaxTminWetBulbday_length_mindayofyearintensity_heavyis_BCis_BRis_DZis_FGis_FUis_GRis_HZis_MIis_RAis_SNis_SQis_TSis_VCmonthweekyear
AvgSpeed1.000-0.071-0.1110.1630.1920.1600.885-0.2710.000-0.262-0.095-0.120-0.065-0.1110.018-0.0980.0520.0000.0370.0530.0670.0200.0000.0780.0390.1770.2910.0000.0900.000-0.104-0.0960.039
Cool-0.0711.0000.836-0.7940.0220.062-0.097-0.2500.050-0.1820.9690.9290.9280.9210.424-0.1730.0770.0000.0660.1670.0440.0000.0000.1020.0500.1170.0000.0000.2540.095-0.178-0.1760.007
DewPoint-0.1110.8361.000-0.7700.2730.018-0.156-0.3700.000-0.3200.8750.7870.9010.9740.373-0.1210.0810.0000.2500.0810.0530.0000.0000.1930.0000.1820.1810.0960.3600.117-0.125-0.1210.002
Heat0.163-0.794-0.7701.0000.0220.0380.1730.1930.0340.136-0.861-0.835-0.831-0.836-0.4720.2240.0000.0000.0340.1970.0150.0000.0000.0960.0000.0690.3250.0000.1450.0000.2250.2240.022
PrecipTotal0.1920.0220.2730.0221.0000.1060.050-0.4160.000-0.4200.028-0.0430.1010.1600.055-0.0580.0000.0000.3630.0220.1580.0000.0700.0000.0000.3290.0000.0000.3560.101-0.056-0.0570.018
ResultDir0.1600.0620.0180.0380.1061.0000.092-0.2500.042-0.2400.0320.0580.0010.027-0.1110.1590.0280.0000.0780.0380.0250.0570.0000.0680.0000.1460.0630.0000.1990.0530.1610.1590.007
ResultSpeed0.885-0.097-0.1560.1730.0500.0921.000-0.1570.000-0.164-0.118-0.139-0.088-0.142-0.030-0.0340.0330.0000.0690.0360.0610.0000.0000.0960.0000.0910.2540.0000.0420.000-0.039-0.0340.024
SeaLevel-0.271-0.250-0.3700.193-0.416-0.250-0.1571.0000.0000.960-0.270-0.228-0.295-0.331-0.2210.1920.0000.0000.2670.1310.0440.0000.0000.0760.0000.3250.0400.0120.2500.0220.1860.193-0.055
Station0.0000.0500.0000.0340.0000.0420.0000.0001.0000.1890.0540.0000.1110.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0590.0000.0000.0130.0000.0000.0140.0000.0000.000
StnPressure-0.262-0.182-0.3200.136-0.420-0.240-0.1640.9600.1891.000-0.201-0.177-0.214-0.271-0.1880.1710.0210.0000.2720.1280.0430.0000.0000.0720.0000.3270.0360.0660.2380.0090.1660.171-0.056
Tavg-0.0950.9690.875-0.8610.0280.032-0.118-0.2700.054-0.2011.0000.9630.9610.9560.467-0.2140.0840.0740.0790.2030.0600.0000.0000.1140.0000.1130.2750.0000.2460.087-0.216-0.2160.001
Tmax-0.1200.9290.787-0.835-0.0430.058-0.139-0.2280.000-0.1770.9631.0000.8520.8900.458-0.2390.0460.0140.0800.2580.0340.0000.0000.0920.0000.1110.2300.0450.2060.062-0.241-0.2420.004
Tmin-0.0650.9280.901-0.8310.1010.001-0.088-0.2950.111-0.2140.9610.8521.0000.9510.447-0.1770.1160.0320.1140.1370.0890.0000.0000.1320.0130.1090.2550.0000.2400.099-0.180-0.178-0.004
WetBulb-0.1110.9210.974-0.8360.1600.027-0.142-0.3310.000-0.2710.9560.8900.9511.0000.429-0.1680.0940.0000.1200.1510.0620.0000.0000.1620.0000.1260.3130.0530.3190.129-0.171-0.170-0.000
day_length_min0.0180.4240.373-0.4720.055-0.111-0.030-0.2210.000-0.1880.4670.4580.4470.4291.000-0.8220.0690.0680.0510.1830.0670.0680.0000.0690.0510.0760.1670.0000.1680.041-0.823-0.8220.003
dayofyear-0.098-0.173-0.1210.224-0.0580.159-0.0340.1920.0000.171-0.214-0.239-0.177-0.168-0.8221.0000.0820.0490.0790.1510.0730.0940.0100.0610.0000.1210.1240.0380.1720.0610.9860.999-0.003
intensity_heavy0.0520.0770.0810.0000.0000.0280.0330.0000.0000.0210.0840.0460.1160.0940.0690.0821.0000.1400.1430.0530.8040.0000.0000.0740.2020.0240.0000.0000.0430.0000.0630.0700.000
is_BC0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0140.0320.0000.0680.0490.1401.0000.0460.0000.2740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0400.018
is_BR0.0370.0660.2500.0340.3630.0780.0690.2670.0000.2720.0790.0800.1140.1200.0510.0790.1430.0461.0000.2360.1740.0280.0000.3250.0210.4640.0000.0350.2860.0680.0710.0680.108
is_DZ0.0530.1670.0810.1970.0220.0380.0360.1310.0170.1280.2030.2580.1370.1510.1830.1510.0530.0000.2361.0000.0330.0000.0000.0530.0000.2000.0000.0000.0380.0000.1560.1590.149
is_FG0.0670.0440.0530.0150.1580.0250.0610.0440.0000.0430.0600.0340.0890.0620.0670.0730.8040.2740.1740.0331.0000.0000.0000.1010.1650.0490.0000.0000.0670.1050.0620.0620.000
is_FU0.0200.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0680.0940.0000.0000.0280.0000.0001.0000.0000.1080.0000.0000.0000.0000.0000.0000.0650.1080.062
is_GR0.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.003
is_HZ0.0780.1020.1930.0960.0000.0680.0960.0760.0590.0720.1140.0920.1320.1620.0690.0610.0740.0000.3250.0530.1010.1080.0001.0000.0000.0610.0000.0000.0940.0570.0660.0590.126
is_MI0.0390.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0510.0000.2020.0000.0210.0000.1650.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
is_RA0.1770.1170.1820.0690.3290.1460.0910.3250.0000.3270.1130.1110.1090.1260.0760.1210.0240.0000.4640.2000.0490.0000.0000.0610.0001.0000.0500.0270.4550.0740.1090.1140.021
is_SN0.2910.0000.1810.3250.0000.0630.2540.0400.0130.0360.2750.2300.2550.3130.1670.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0501.0000.0000.0000.0000.0920.1280.011
is_SQ0.0000.0000.0960.0000.0000.0000.0000.0120.0000.0660.0000.0450.0000.0530.0000.0380.0000.0000.0350.0000.0000.0000.0000.0000.0000.0270.0001.0000.0000.0000.0270.0300.000
is_TS0.0900.2540.3600.1450.3560.1990.0420.2500.0000.2380.2460.2060.2400.3190.1680.1720.0430.0000.2860.0380.0670.0000.0110.0940.0000.4550.0000.0001.0000.1990.1660.1690.061
is_VC0.0000.0950.1170.0000.1010.0530.0000.0220.0140.0090.0870.0620.0990.1290.0410.0610.0000.0000.0680.0000.1050.0000.0000.0570.0000.0740.0000.0000.1991.0000.0680.0830.105
month-0.104-0.178-0.1250.225-0.0560.161-0.0390.1860.0000.166-0.216-0.241-0.180-0.171-0.8230.9860.0630.0470.0710.1560.0620.0650.0000.0660.0000.1090.0920.0270.1660.0681.0000.985-0.002
week-0.096-0.176-0.1210.224-0.0570.159-0.0340.1930.0000.171-0.216-0.242-0.178-0.170-0.8220.9990.0700.0400.0680.1590.0620.1080.0000.0590.0000.1140.1280.0300.1690.0830.9851.000-0.001
year0.0390.0070.0020.0220.0180.0070.024-0.0550.000-0.0560.0010.004-0.004-0.0000.003-0.0030.0000.0180.1080.1490.0000.0620.0030.1260.0000.0210.0110.0000.0610.105-0.002-0.0011.000

Missing values

2026-01-07T23:47:59.888976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-07T23:48:00.263469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StationDateTmaxTminTavgDewPointWetBulbHeatCoolSunriseSunsetPrecipTotalStnPressureSeaLevelResultSpeedResultDirAvgSpeedyearmonthweekdayofyearday_length_minis_FCis_TSis_GRis_RAis_DZis_SNis_SGis_GSis_PLis_ICis_FGis_BRis_UPis_HZis_FUis_VAis_DUis_DSis_POis_SAis_SSis_PYis_SQis_DRis_SHis_FZis_MIis_PRis_BCis_BLis_VCintensity_heavyintensity_light
012007-05-0128.310.019.210.613.30.01.104:48:0018:49:000.00985.429.822.72714.82007518121841.0000000000000000000000000000000000
122007-05-0128.911.120.010.613.90.01.704:48:0018:48:000.00988.129.824.32515.42007518121840.0000000000000000000000000000000000
212007-05-0215.05.610.35.68.37.80.004:47:0018:50:000.00994.930.0920.9421.62007518122843.0000000000001000000000000000000000
322007-05-0215.66.110.85.68.37.20.004:46:0018:49:000.00997.030.0821.4221.62007518122843.0000000000001010000000000000000000
412007-05-0318.97.813.34.48.95.00.004:46:0018:51:000.00995.330.1218.8719.22007518123845.0000000000000000000000000000000000
522007-05-0319.48.914.24.410.03.90.004:45:0018:51:000.00997.630.1220.8621.22007518123846.0000000000000010000000000000000000
612007-05-0418.99.414.25.010.03.90.004:44:0018:52:000.03992.630.0516.7817.42007518124848.0000100000000000000000000000000000
712007-05-0518.911.715.33.39.42.80.004:43:0018:53:000.03995.630.1018.8719.32007518125850.0000000000000000000000000000000000
822007-05-0518.912.215.63.910.02.80.004:42:0018:53:000.03997.630.0918.0718.52007518125851.0000000000000000000000000000000000
912007-05-0620.09.414.7-1.17.83.30.004:42:0018:55:000.001001.430.2923.21124.12007518126853.0000000000000000000000000000000000
StationDateTmaxTminTavgDewPointWetBulbHeatCoolSunriseSunsetPrecipTotalStnPressureSeaLevelResultSpeedResultDirAvgSpeedyearmonthweekdayofyearday_length_minis_FCis_TSis_GRis_RAis_DZis_SNis_SGis_GSis_PLis_ICis_FGis_BRis_UPis_HZis_FUis_VAis_DUis_DSis_POis_SAis_SSis_PYis_SQis_DRis_SHis_FZis_MIis_PRis_BCis_BLis_VCintensity_heavyintensity_light
291012014-10-2725.010.617.810.614.40.60.006:18:0016:53:000.00979.329.6619.31920.820141044300635.0000000000000000000000000000000000
291122014-10-2726.112.219.211.115.00.01.106:17:0016:51:000.51982.129.6720.41921.920141044300634.0000100000000000000000000000000000
291212014-10-2820.07.213.63.38.34.40.006:19:0016:51:000.03987.129.8523.82625.120141044301632.0000000000000000000000000000000000
291322014-10-2818.98.913.94.48.94.40.006:18:0016:50:000.76989.829.8522.52623.520141044301632.0000100000000000000000000000000000
291412014-10-299.42.25.80.04.412.20.006:20:0016:50:000.00994.230.0615.32915.920141044302630.0000000000000000000000000000000000
291522014-10-299.44.46.91.15.611.10.006:19:0016:48:000.00996.330.0713.72914.520141044302629.0000000000000000000000000000000000
291612014-10-3010.60.05.31.14.412.80.006:22:0016:49:000.00993.630.098.2248.920141044303627.0000000000000000000000000000000000
291722014-10-3011.72.87.21.75.611.10.006:21:0016:47:000.03995.930.109.52310.520141044303626.0000100000000000000000000000000000
291812014-10-318.30.64.4-3.90.613.90.006:23:0016:47:000.76998.630.2036.43436.920141044304624.0000101000000000000000000000000000
291922014-10-319.41.15.3-1.72.212.80.006:22:0016:46:001.021000.330.2034.93436.420141044304624.0000101000001000000000000000000000